In order to cut down on costs to the greatest possible extent, enterprises hope to distribute goods to different customers at the lowest costs possible. Based on this, this paper proposes an optimal location method for an intelligent transportation logistics warehouse. This scheme combines a variety of complex mechanisms to allow IoT devices to provide input. The scheme makes full use of the irreducibility of a blockchain system to promote the development and design of blockchain logistics applications. This method is aimed at tracking the progress of transportation of products in the whole supply chain. Experimental results show that, compared with traditional methods, the optimal positioning method has the advantages of fewer calculations, a high positioning accuracy, and a low overall cost, and it obtains the best warehouse positioning results. Based on the Internet of Things and blockchain technology, the application of intelligent logistics systems enables enterprises to intuitively understand their current inventory and the transportation status of goods, thus better controlling changes in enterprise resources.

Background: Colorectal cancer (CRC) is the second leading cause of death worldwide, and distant metastasis is responsible for the poor prognosis in patients with advanced-stage CRC. RPS24 (ribosomal protein S24) as a ribosomal protein, multiple transcript variant encoding different isoforms have been found for this gene. Our previous studies have demonstrated that RPS24 is overexpressed in CRC. However, the mechanisms underlying the role of RPS24 in tumor development have not been fully defined. Methods: Expression of RPS24 isoforms and lncRNA MVIH in CRC tissues and cell lines were quantified by real-time PCR or western blotting assay. Endothelial tube formation assay was performed to determine the effect of RPS24 on tumor angiogenesis. The cell viability of HUVEC was determined by MTT assay, and the migration and invasion ability of HUVEC were detected by transwell assay. PGK1 secretion was tested with a specific ELISA kit. Results: Here, we found that RPS24c isoform was a major contributor to tumor angiogenesis, a vital process in tumor growth and metastasis. Real-time PCR revealed that RPS24c isoform was highly expressed in CRC tissues, while other isoforms are present in both normal and CRC tissues with no statistical difference. Moreover the change of RPS24 protein level is mainly due to the fluctuation of RPS24c. Furthermore, we observed that silencing RPS24c could decrease angiogenesis by inhibiting tubule formation, HUVEC cell proliferation and migration. Additionally, we investigated the molecular mechanisms and demonstrated that RPS24c mRNA interacted with lncRNA MVIH, the binding-interaction enhanced the stability of each other, thereby activated angiogenesis by inhibiting the secretion of PGK1. Conclusion: RPS24c facilitates tumor angiogenesis via the RPS24c/MVIH/PGK1 pathway in CRC. RPS24c inhibition may be a novel option for anti-vascular treatment in CRC.

Today, the global exchange market has been the world’s largest trading market, whose volume could reach nearly 5.345 trillion US dollars, attracting a large number of investors. Based on the perspective of investors and investment institutions, this paper combines theory with practice and creatively puts forward an innovative model of double objective optimization measurement of exchange forecast analysis portfolio. To be more specific, this paper proposes two algorithms to predict the volatility of exchange, which are deep learning and NSGA‐II‐based dual‐objective measurement optimization algorithms for the exchange investment portfolio. Compared with typical traditional exchange rate prediction algorithms, the deep learning model has more accurate results and the NSGA‐II‐based model further optimizes the selection of investment portfolios and finally gives investors a more reasonable investment portfolio plan. In summary, the proposal of this article can effectively help investors make better investments and decision‐making in the exchange market.